Multiservice/multirate pure CDMA for mobile communications

1404 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 48, NO. 5, SEPTEMBER 1999

Multiservice/Multirate Pure CDMAfor Mobile Communications

Hedayat Azad,Member, IEEE, A. Hamid Aghvami,Senior Member, IEEE,and William C. Chambers,Member, IEEE

Abstract—Future mobile communication systems should beable to support a wide range of services with different bitrates. Spread-spectrum code-division multiple-access (CDMA)techniques have attracted much attention to be employed insuch a system. Different techniques of CDMA could be usedto map low-, medium-, and high-bit rates data into the sameallocated bandwidth, including pure or wide-band CDMA,FDM/FH/CDMA, TDM/TH/CDMA, or a hybrid of thesetechniques. This paper investigates multirate pure CDMA usingmultiuser interference statistics derived for both Gaussian andRayleigh fading channels. Approximation of multiuser/multipathinterference, in general, helps in the theoretical approach toerror performance evaluation and, in particular, is quite usefulfor simulation approach in a fading channel. Some results ofa multirate pure CDMA system with two services (low- andhigh-bit rates), for both BPSK and DPSK modulation schemes,are presented and compared.

Index Terms—BPSK, code-division multiple access (CDMA),coherent/noncoherent demodulation, convolutional coding, differ-ential phase-shift keying (DPSK), direct sequence, fading channel,multiple-access interference, multiservice/multirate.

NOMENCLATURE

UMTS Universal mobile telecommunicationssystem.

FPLMTS Future public land mobile telecommu-nications system.

IMT2000 International mobile telecommunica-tions 2000.

ETSI RTT European Telecommunications Stan-dardization Institute/Radio Transmis-sion Technology.

FDM/FH/CDMA Frequency-division multiplexing/fre-quency-hopping/code-division multipleaccess.

TDM/TH/CDMA Time-division multiplexing/time-hop-ping/code-division multiple access.

I. INTRODUCTION

FUTURE mobile telecommunication systems(UMTS/FPLMTS/IMT2000) should be able to support

different services with different bit rates including voice,video, and data up to 2 Mb/s in different environments [1].There are different multiple-access techniques which have

Manuscript received September 5, 1996; revised August 18, 1998.H. Azad is with Wireless Facilities, Inc., San Diego, CA 92121 USA.A. H. Aghvami and W. C. Chambers are with the Centre for Telecommuni-

cations Research, King’s College, University of London, London WC2R 2LS,U.K.

Publisher Item Identifier S 0018-9545(99)05719-9.

been proposed for these systems; among them time-divisionmultiple access (TDMA) and code-division multiple access(CDMA) seem to be the main contenders [2]. In Europe,there were four different radio access techniques proposed:W-CDMA (Alpha concept), BDMA (TDMA/OFDM, Betaconcept), wide-band TDMA (Gamma concept), and TD-CDMA (Delta concept) [3]–[6]. An agreement was reachedby consensus in an ETSI SMG meeting, which was heldon January 28–29, 1998 in Paris, France, to use wide-bandCDMA (W-CDMA) in the paired band [frequency-divisionduplex (FDD)] and TD-CDMA in the unpaired band [time-division duplex (TDD)]. The ETSI RTT proposal, UMTSterrestrial radio access (UTRA) [7], was submitted to ITUin June 1998. In Japan, NTT DoCoMo’s proposal “coherentmulticode DS-CDMA mobile radio access” [8] was alwaysa strong candidate for Japanese third-generation radioaccess technique while TDMA-based systems were still incontention. Japan’s proposal for candidate Radio TransmissionTechnology on IMT2000, by Association of Radio Industriesand Businesses (ARIB), was submitted to the Internationaltelecommunications Union (ITU) as well in June 1998 [9].In the United States, both wide-band cdmaOne, backedby the CDMA Development Group (CDG), and an IS-136TDMA-based system, backed by the Universal WirelessCommunications Consortium (UWCC), had been consideredand final proposals were submitted to ITU in June 1998[10], [11]. Other U.S. proposals included wireless multimediaand messaging services (WIMS)/wide-band CDMA [12] andNorth American wide-band CDMA [13]. There are some otherproposals submitted to ITU. Those include Global CDMA Iand Global CDMA II by South Korean TTA [14], [15], time-division synchronous CDMA (TD-SCDMA) by the ChinaAcademy of Telecommunications Technology (CATT) [16],and European digital enhanced cordless telecommunications(DECT), which have been proposed for indoor and pedestrian(not vehicular) environments only [17]. Looking at differentsubmitted proposals, it is clear that seven out of nine proposalsare based on CDMA which makes this technology the mostlikely multiple-access technique to be employed for third-generation system’s radio interface. One of the main problemsin CDMA technique is how to define logical channelsfor different bit rates and how to map them into physicalchannels in the same allocated bandwidth. There are differentapproaches to solve this problem. All services’ data bits withdifferent rates can be spread to the whole allocated bandwidth(pure CDMA or multirate broad-band CDMA). Bandwidthcan be divided into smaller fixed or hopping subchannels over

0018–9545/99$10.00 1999 IEEE

AZAD et al.: MULTISERVICE/MULTIRATE PURE CDMA FOR MOBILE COMMUNICATIONS 1405

which low- and medium-bit rates can be spread (frequency-division approaches, FDM/FH/CDMA). Time can be dividedinto smaller time slots, fixed or hopping, during whichlow- and medium-bit rates can be spread and transmitted(time-division approaches, TDM/TH/CDMA). Finally, anycombination of these approaches can be considered [18].The subject of this paper is an investigation into the firstapproach, namely, multirate pure CDMA. Since CDMAtechniques are interference limited rather than band limited,the investigation begins in Section II with an approximationof the interference imposed on the desired signal by otherusers called multiple access or multiuser interference. Theresults of Section II are used in Section III to discuss theperformance of a multirate system in a Gaussian channelboth analytically and by simulation. Since a multipath fadingchannel is considered and since simulating the interferenceof all other users of all other services coming from differentpaths is very time consuming, the results of Section II will beused to introduce a model to approximate this interference.This is done in Section IV. In Section V, using the resultsof the previous section’s approximation, the performanceof a multirate system in a fading channel is analyzed bothanalytically and with the help of simulation. BPSK andDPSK modulation schemes are both considered. Then theresults of the investigation for a two-service multirate system(i.e., voice and high-bit rate data) are compared. Finally,Section VI is the conclusion.

II. M ULTIUSER INTERFERENCE

APPROXIMATION IN A GAUSSIAN CHANNEL

In order to get the statistics of interference from other usersin a direct sequence CDMA system, the analysis begins byconsidering a code with length, which is used, with differentcode shifts by all users. is the number of chips per bit ofdata and is the number of bits ( chip sequences) pera code length after which code is repeated again. To calculatethe interference of one interfering user, suppose the interferingsignal’s delay related to the desired signal is chipduration. Values of and can be varied in the ranges of

and to give allpossible time shifts to the fraction of a chip duration (i.e.,in an asynchronous system). An analysis similar to that in[19] and [20] shows that the decision variable at the samplinginstant at the output of the demodulator forth data bit inthe code length contains a multiple-access interference termproportional to

(1)

Here, is the discrete aperiodic cross-correlationfunction defined in details in [19], is the phase shift betweenthe desired and the interfering signal carrier frequencies, andvariables and are two successive data bits of interferingsignal taking values and

is a random variable and its variance as a function of theinterfering signal’s delay can be determined. and areindependent random variables withand taking any integervalue in the ranges and and uniformlydistributed on (0, 1). It is clear that

(2)

Using the formula for it can be shown thatthis variance for chip asynchronous systems is one third thevariance for chip synchronous systems. Using (1) and (2) let

be the expansion of the term below, which is the right-handside part of (2)

(3)

It is easy to show that the first and second terms in bracketin (3) are equal and equal to cross-correlation parameter

defined in [19], and the third term in bracketsis equal to which for most applications is approx-imated to zero. On the other hand

and

Regarding the cross-correlation parameter definition andusing the integral results above in (3), for an asynchronoussystem

If in (3) , for a chip and frequency synchronizedsystem


Fig. 1. Interference variance versus processing gain.

Fig. 2. Maximum number of users versus processing gain in a Gaussianchannel.

It comes to this conclusion that

(4)

Using the central limit theorem, it can easily be shownthat the total interference imposed by other users on thedesired signal is a zero-mean Gaussian random variable.To calculate the variance of this total interference, it isenough to calculate for chip-synchronized crosscorrelation, to average it over all possible locations ofin thecode length, to multiply it by the number of interfering usersand finally by the factor of 1/3. With the total interference

(5)

where is the number of users.This variance, for one interfering user, has been calculated

by simulation for maximal-length sequences of lengthfor different , as shown in Fig. 1, and the results areconsistent with the approximation of introduced in[19].

III. M ULTIRATE ERROR PERFORMANCE

IN A GAUSSIAN CHANNEL

It begins by considering several services (i.e., voice, video,and higher bit rates data) sharing the same allocated bandwidthusing pure CDMA as the multiple-access technique. In thiscase, suppose user 1 of service 1 is the desired user. There are

different sources of impairment affecting the desiredsignal, where is the number of different services. Theseare Gaussian noise, interference by other service 1 users,and interference by users of other services. As wasdiscussed before in the sampling instant of desired signal atdemodulator, the noise and interference appear asindependent Gaussian random variables with zero mean andvariances

It is known that

where is noise double-sided power spectral density andis desired users bit energy. If is the equivalent of ,

but for all sources of interference and “SNR” is the bit energyto in decibels (for simplicity in formulas)

SNR

(6)

Then if all ’s are known the probability of error forBPSK and DPSK modulations as a function of couldbe calculated, respectively, by

and (7)

Suppose in a particular case the number of servicesisequal to two. Ten-kb/s voice and 250-kb/s high-bit rate dataare considered. Let the number of users for service 1 and 2 be

and accordingly and the powers for these services beand is the voice activity factor for voice users (0.5

in this investigation) and is the sectorization factor for bothvoice and data users (2.75 for a three-sector cell). and

are multiuser interference variances for services 1 and 2,calculated as described in Section II, which are a function ofprocessing gains and accordingly. and(in decibels) are called SNRand SNR for simplicity where

and are energies per bit of information for services 1and 2. Now the variances of different sources of impairmentcan be calculated and used in the probability of error formula

(8)


(a) (b)

Fig. 3. Bit error performance in a Gaussian channel. (a) Low-bit rate (voice). (b) High-bit rate (data).

Here is calculated as

and

where and are bit durations.If the powers are normalized to desired user’s power

then

(9)

The probability of error, for and, for 10-kbps voice and andfor 250-kbps data (10-MHz bandwidth) with dif-

ferent number of users have been investigated. Fig. 2 showsa maximum number of users for voice and data, with 10and 10 bit error probability requirements, for differentprocessing gains and BPSK modulation.

In Fig. 3, bit error probabilities for both voice and data,in a particular case of 500 voice users and ten data usersfor both BPSK and DPSK modulations, are shown. Thetheoretical results using the interference approximation arematched closely with the results of accurate simulation withoutusing approximation. The slight difference in higher bit energyto noise ratios is for a relatively higher contribution of otherusers interference in the total noise power which is not pureGaussian anymore. Bit error rate (BER) results for both voiceand data, for BPSK and DPSK, using convolutional coding arealso presented. A rate 1/2 constraint length 7 convolutionalencoder with both a hard- and soft-decision Viterbi decoderhas been used.

It can be noticed that in a Gaussian channel the less than3-dB degradation from BPSK to DPSK, related to narrow-bandtransmission, is increased to 5–6 dB for wide-band spread-spectrum CDMA transmission and further to 7–8 dB whenconvolutional encoding/Viterbi decoding is used. This indi-cates that the improvement by encoding/decoding for DPSKmodulation in a multirate CDMA system, in the presence

of multiuser interference, is less than BPSK (3-dB DPSKperformance improvement compared to 5 dB for BPSK in10 BER for voice and 5-dB improvement compared to 8dB in 10 BER for data). The more multiuser interference(higher number of users), the less is this improvement. It canbe seen that in both BPSK and DPSK, soft-decision decodinghas 2–3-dB improvement with respect to hard decision. It isalso shown that for BPSK modulation the BER requirementsfor voice and data, 10 and 10 , respectively, are achievedat approximately SNR dB and SNR dB.

IV. M ULTIUSER/MULTIPATH INTERFERENCE

APPROXIMATION IN A FADING CHANNEL

It was seen in Section II that in an additive white Gaussianchannel other users interference can be approximated as aGaussian-distributed random variable with zero mean andvariance proportional to cross correlation of code sequencesused. In this section, the interference inflicted on a desiredsignal in a fading channel for a multirate CDMA systemwill be looked at. Then the possibility of approximatingthis interference as a random variable will be considered aswell. This helps to reduce considerably running time of thesimulation of such a system, which would otherwise be verytime consuming. The bit error performance of such a systemwill be analyzed in the next section. The analytical approachwill then be compared with the results of the simulation. Fig. 4is a simple model for one path of the desired signal.

In the figure

(10)

where and are data symbols for in-phase and quadraturephase channels, is the number of code chips in a databit, and is the notation for the th chip


Fig. 4. A simple fading channel model.

in the chips code sequence The model for the fadingchannel in this investigation is the Jake’s model [21]. Thismodel produces two in-phase and quadrature bandsand

from which consecutive samples in time, shown here byand , are taken. Then the in-phase and quadrature basebandsignals, and , are multiplied (complex multiplication) byand to produce the baseband channel output. In this model,

and are Gaussian processes and the envelope of thefading gains are Rayleigh distributed. Now as in Fig. 4, it isassumed that and are the fading signals for oneof the desired signal paths andth interfering path. Then thebaseband signals and at the input of demodulator are

(11)

where is the carrier phase shift between desired and interfer-ing signal and and are channel Gaussian noises for in-phase and quadrature channels. After complex multiplicationof and by and (receiver’s channel estimationand phase compensation) and despreading, The demodulator’soutput at the sampling instant are random variablesandThese random variables consist of an additive Gaussian noisepart and an interference part. In order to look at the probabilitydensity function of the interference parts inand and tocalculate their mean and variance,is treated at first. It iseasy to show that

(12)

In , is a random variable uniformly distributed betweenzero and two. The interference part in consists of eightrandom elements. Each random element is a multiplication offour random variables. If the codes are treated as a familyof random sequences and the system as an asynchronoussystem then all the terms undercan in fact be approximatedto random variables with zero mean and variance

calculated in Section II. and producedby the Jake’s fading model are in fact Gaussian-distributedrandom variables being low-pass filtered (especially whenaveraging in a long time for BER performance purposes)with zero mean and variance [21]. Finally,and are random variables equally likely or withzero mean and variance for BPSK/DPSK and

for QPSK. The eight random elements in (12)have zero mean and equal variances with the distribution otherthan Gaussian. They have a bell-shape probability distributionfunction, which is narrower at the top and wider at the bottomcompared to a Gaussian distribution function with the samevariance. Although the elements are not Gaussian distributed,there are eight of these elements in the interference of justone interfering path and many interferences from many usersand their different paths would contribute to the total levelof interference. Using the central limit theorem again, it canbe shown that the total interference can be approximated asa Gaussian random variable. Fig. 5 shows the distributionfunctions of one random element, eight random elements (oneinterfering path) and 40 random elements (five interferingpaths or just two interfering users for a two–three-pathsmultipath channel) compared to Gaussian random variableswith the same variance.

Repeating the same calculation forand taking into accountvariances of different random variables it is quite easy toshow that the variance of interference due to one path of oneinterfering user on one desired user’s received path for bothin-phase and quadrature channels is equal to

and

(13)

For QPSK and the only channel of BPSK/DPSK, respec-tively. and are as defined in Section III.

One should notice that this variance should be multipliedby both the number of interfering users and their interferingpaths to give the total interfering power on one path of thereceived desired signal. These results could be used for theanalysis of a multirate CDMA system error performance whereanalytical approach is used. In the case of simulation onecan suppose that and , for different paths of the desiredsignal, are known through channel estimation (or in fact getthese parameters by using a channel estimator block in thesimulation). Then the parameters shown asand in (12)could be taken out, since they are not regarded as randomvariables any more, and their actual estimated values couldbe used. In other words, in a complicated simulation, frombeginning to the end, it is only the desired signal which goesthrough the whole process, including fading channel and a


(a) (b)

(c)

Fig. 5. The approximation of multipath interference as Gaussian random variable.

several-arms RAKE receiver. All other interfering users withall their interfering paths are regarded as random variables,and their fairly accurate statistics are used to approximate theirinterference power. This saves a huge amount of simulationtime. Using this assumption it can be shown that ifis thenumber of different services, if is the number of users inth service, and if is the number of paths for users of theth service in a BPSK system, then the received desired signal

at the sampling instant is

(14)

where is the power gain for theth path of the th service(multipath profile of different services) and is the proportionof the received power of theth service users with respectto desired user shown in (9) in Section III. is multipathinterference whose variance this time is equal to

(15)

The interference of the desired signal’s other paths ondesired path is negligible and has not been considered in (14).

V. MULTIRATE ERROR PERFORMANCE IN A FADING CHANNEL

In this case, all definitions of Section III hold except thedefinition of the channel which changes to a wide-sense sta-tionary uncorrelated scattering (WSSUS) frequency-selectiveRayleigh-distributed fading channel with number of paths.

is desired signal received SNR per bit of information. ForBPSK and DPSK under consideration the conditional error


probability functions are

and (16)

Conditioned on to be fixed. When is a randomvariable, as in this case, the conditional error probabilityfunction should be averaged over the probability densityfunction of Now

(17)

where is the desired signal bit energy, is the power gainfor the th path, and is received SNR from th path andis distributed according to chi-squared distribution with twodegrees of freedom (for Rayleigh-distributed envelope).

is the average SNR for theth path and is defined as

when is the expectation value.In [22], the probability density function of is derived and

it has been shown that when the conditional error probabilitiesin (16) are averaged over the probability density function of

the results for BPSK and DPSK are

(18)

and

(19)

respectively, where

and

(20)

Therefore, for calculating the probability of bit error withrespect to averaged , for a multiservice/multirateCDMA system in a fading channel we have the follow-ing: first, using the multipath interference approximation de-rived in Section IV, the equivalent averaged signal energy to“noise interference” ratio should be calculatedas in Section III. Then having the characteristics of the mul-tipath channel (i.e., power levels for different arriving paths),each could be worked out for various averagedand beused in above error probability formulas.

As a particular case, the error performance of a two-servicemultirate system (as in Section III) has been investigatedfor BPSK and DPSK modulations both theoretically and bysimulation using the approximations described in Sections IIand IV-A voice user BER against averaged received bit energyto noise ratio for 200 voice and five data users and BPSKmodulation is plotted in Fig. 6 for one-, two-, and three-armsRAKE receiver. The channel is a frequency-selective Rayleigh

Fig. 6. Multirate BER performance in a fading channel.

fading channel with its different paths average power takenfrom JTC channel model for outdoor residential/urban high-rise areas [23], which are (0,0.4) and (0, 0.4, 6) dB fortwo- and three-paths models. Maximum Doppler shiftHz. No means of performance improvement like coding andinterleaving has been used in this example in order to be ableto compare theoretical and simulation results accurately.

As it shows, these results match closely and confirm that theaccuracy of interference approximation in fading environmentis satisfactory. Note that in Fig. 6 SNRis always SNRdB.

In Fig. 7, error performance for a voice user and a datauser in a system with 500 voice and five data users hasbeen plotted. Curves for three-arms RAKE receiver with andwithout convolutional encoding/soft-decision Viterbi decodingand also interleaving have been plotted for both BPSK andDPSK. Again, rate 1/2 convolutional encoder with the con-straint length of 7 has been used. 2816 and 28 400 blockinterleavers have been used for voice and data respectively. Itcould be noticed that the degradation due to use of DPSKmodulation increases slightly in a fading channel (in the worstcase from 8 dB for Gaussian channel [Fig. 3(b)] to 10 dB at10 BER for high-rate data). It can also be seen that forBPSK, average signal to noise ratios of 11 dB for voice and14 dB for data could result the required BER’s. But for DPSKthese numbers increase to 20 dB for voice and 25 dB fordata. It means that the maximum number of user decreasedramatically for DPSK compared to BPSK.

Fig. 8 shows a voice bit error probability against signal tonoise ratios for voice and data in a system with 500 voiceand ten data users. This kind of curves are useful to find theacceptable regions for different services (in this case,the points below 10 horizontal surface for voice). We couldhave plotted the curve for data BER and found the acceptableregion for data (below 10 horizontal surface this time). Theintersection between these two regions could be the acceptableregion for the system.

In Fig. 9(a), the number of high-bit rate versus low-bit rateusers, for both BPSK and DPSK, is plotted to have an idea


(a) (b)

Fig. 7. Bit error performance in a fading channel. (a) Low-bit rate (voice). (b) High-bit rate (data).

Fig. 8. Low-bit rate BER performance in a fading channel.

of the whole capacity of the system. Capacity in a Gaussianchannel is plotted as well as a reference. It is interesting to seethat all these curves are approximately straight lines and eachpoint on the line can give simultaneous number of users forboth services. It is obvious that in a Rayleigh fading channelthe capacity for noncoherent DPSK is approximately half ofthat for coherent BPSK. Since the “average duration of fades”(deducible from fade level crossing rate) to “symbol duration”proportion is far more for high-bit rate users than low-bitrates, it affects interleaving capability to reduce BER forthe former. Further improvement would have been achievedhad a larger interleaver been used for high-bit rates data. InFig. 9(b), the same curves as Fig. 9(a) have been plotted,but this time for a three-services system (10-kbps voice with

, 64-kbps video with , and 250-kbps data with ). It can be seen that the curvesfor BPSK and DPSK are two approximately flat surfacesand the operating point could move along these surfaces. Itshould be noticed that for these results cochannel interferencehas not been considered and near perfect channel estimationand power control has been assumed. This investigation hasfocused on the physical layer and a single-cell system hasbeen considered. Since in a pure CDMA system intercell

(a)

(b)

Fig. 9. Approximate number of users in a fading channel.

interference (cochannel interference) is a calculable proportion(approximately 0.6) of intracell interference (multiple-accessinterference), the application of this investigation’s approach


Fig. 10. Maximum number of users versus processing gain in a fadingchannel.

to a cellular system, regarding multiple-access interference, isquite straightforward. It is enough to multiply the varianceof multiple-access interference by the factor of 1.6 for bothmathematical analysis and simulation. Consequently, furtherdegradation in error performance, hence, further reduction inthe capacity is expected in a practical cellular system.

Finally, Fig. 10 shows the maximum number of users forthese services (the same conditions) against processing gainfor BPSK modulation. These curves could be plotted for dif-ferent BER requirements and give anapproximate maximum number of users, in a fading channel,for an available processing gain.

VI. CONCLUSIONS

The performance of a multirate pure CDMA system, forboth BPSK and DPSK modulations, was investigated. The ap-proximation of multiuser interference was discussed generallyand in particular this approximation for a fading channel wasintroduced. The way of using statistics of multiuser/multipathinterference in the simulation was discussed, which is themiddle way between pure theoretical approach and completesimulation and is believed to be very suitable for error per-formance investigation of a complicated multiservice/multirateCDMA system. Results presented proved its accuracy. Someresults were presented for a two service (low- and high-bit rates) system. This investigation was focused on thephysical layer and a single-cell system was considered. Thereare other factors affecting (and limiting) the capacity ofpractical cellular systems. These are both physical and upperlayer’s related factors. Power control, channel estimation,soft- and hard-handoff algorithms, cell planning parameters,cell loading parameters, optimization, etc., are among thesefactors (system level considerations). It was not the intentionof this paper to give the exact practical number of usersfor such a system or to maximize the capacity. These couldhave been achieved by considering a practical cellular systemand using more powerful coding, interleaving and forwarderror correcting techniques (especially for higher bit rates)

which were beyond the scope of this paper. It was intendedto open a way of investigation, both theoretically and bysimulation, into the performance of multirate CDMA systems,particularly in fading environments. It was also intended togive some preliminary results to be a base of comparisonwith the results of other multiservice/multirate techniques suchas FDM/FH/CDMA, TDM/TH/CDMA, and hybrid systemswhich are currently under investigation, by the authors. Morerealistic results could be expected if all factors mentionedabove are considered in a practical cellular system. The resultsshowed that the severity of fading affects higher bit rates morewhere obviously the processing gain is less. It also indicatedthat the degradation of DPSK relative to BPSK is larger forwide-band spread-spectrum transmission, in the presence ofmultiuser interference, compared to conventional narrow-bandtransmission. DPSK further degrades as the system movesfrom Gaussian to fading channel and as the same conventionaltechniques of performance improvement (e.g., convolutionalencoding/ Viterbi decoding) are used for both systems. Higherbit rates, regardless of the mapping techniques employed,would most likely be the only service to be spread overthe whole allocated bandwidth. Since with less processinggain, their power should be relatively higher (hence, theyare the biggest source of multiuser interference), investigationinto techniques of high-bit-rate interference cancellation fromcomposite multirate signal would be beneficial to all thesemapping techniques.

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[22] J. G. Proakis,Digital Communications. New York: McGraw-Hill,1989.

[23] K. Pahlavan and A. H. Levesque,Wireless Information Networks. NewYork: Wiley, 1995.

Hedayat Azad(S’91–M’94) received the M.Sc. andPh.D. degrees in electronics and telecommunica-tions from King’s College London, University ofLondon, London, U.K., in 1978 and 1996, respec-tively.

In 1978, he joined Toshiba as an ElectronicsEngineer. In 1979, he joined the R&D Laboratory,Grundig, as a Senior Research Engineer. From 1981to 1983, he was a Lecturer at the Tehran Instituteof Technology, where he taught wireless telecom-munications, digital communications, and radio and

television technologies. In 1983, he was one of the founders of GigaElectronics Corporation, where he was the Director of R&D and then VicePresident of Technology on design and implementation of a wide range oftelecommunications products. In 1991, he joined King’s College London as aResearch Fellow, where he worked extensively in industry-supported projectson different multiple-access techniques for second- and third-generationmobile communication systems. Since 1997, he has been with the WirelessFacilities, Inc., San Diego, CA, where he is the Director of AdvancedTechnologies. His current interests include wide-band CDMA, multiple-accessprotocols, bandwidth-efficient modulation and coding techniques, fading anddiversity reception techniques, and high-bit rate data transmission.

A. Hamid Aghvami (M’87–SM’91) received theM.Sc. and Ph.D. degrees from King’s College,University of London, London, U.K., in 1978 and1981, respectively.

In April 1981, he joined the Department of Elec-tronic and Electrical Engineering, King’s College, asa Post-Doctoral Research Associate. He worked ondigital communications and microwave techniquesprojects sponsored by EPSRC. He joined the aca-demic staff at King’s College in 1984. In 1989,he was promoted to Reader and to Professor in

Telecommunications Engineering in 1992. He is presently the Director ofthe Centre for Telecommunications Research, King’s College. He carries outconsulting work on digital radio communication systems for both British andinternational companies. He has published more than 200 technical papersand lectures on digital radio communications including GSM 900/DCS 1800worldwide. He leads an active research team working on numerous mobileand personal communication system projects for third-generation systems,and these projects are supported both by the government and industry. He isa distinguished Lecturer of the IEEE Communications Society.

Dr. Aghvami has been a member, Chairman, and Vice Chairman of thetechnical program and organizing committees of a large number of interna-tional conferences. He is also the Founder of the International Conference onPersonal Indoor and Mobile Radio Communications. He is a Fellow Memberof the IEE.

William G. Chambers (M’78) was born in Leeds,U.K., in 1937. He received the Bachelor degree inmathematics in 1959 and the Ph.D. degree in 1963,both from the University of Cambridge, Cambridge,U.K.

He was a Lecturer in Mathematics at WestfieldCollege and at Royal Holloway and Bedford NewCollege, both at the University of London, until histransfer to the Electronic Engineering Department,King’s College London, London, U.K., in 1986.He has worked in a number of fields including

solid-state physics and Fourier transform spectroscopy. Currently, his maininterests are in coding theory in cryptology, chaos theory, and spread-spectrumcommunications. He is the author ofBasics of Communications and Coding(Oxford, U.K.: Oxford University Press, 1985) and of over 40 articles inrefereed journals.

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Multiservice/multirate pure CDMA for mobile communications